Inter-Call Mobility model: A spatio-temporal refinement of Call Data Records using a Gaussian mixture model

With global mobile phone penetration nearing 100%, cellular Call Data Records (CDRs) provide a large-scale and ubiquitous, but also sparse and skewed snapshot of human mobility. It may be difficult or inappropriate to reach strong conclusions about user movement based on such data without proper understanding of user movement between call records. Based on an analysis of a real-world trace, we propose a novel, probabilistic Inter-Call Mobility (ICM) model of users' position in between calls. The ICM model combines Gaussian mixtures to build a general, comprehensive spatio-temporal refinement of CDRs.We demonstrate that ICM model's application yields strikingly different conclusions to the existing models when applied to basic CDR analyses, such as user proximity probability.

[1]  N. Henze.  A MULTIVARIATE TWO-SAMPLE TEST BASED ON THE NUMBER OF NEAREST NEIGHBOR TYPE COINCIDENCES , 1988 .

[2]  Carlo Ratti,et al.  Transportation mode inference from anonymized and aggregated mobile phone call detail records , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[3]  Hui Zang,et al.  Mining call and mobility data to improve paging efficiency in cellular networks , 2007, MobiCom '07.

[4]  A. Wolisz,et al.  Primary Users in Cellular Networks: A Large-Scale Measurement Study , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[5]  Lukas Kencl,et al.  Active GSM cell-id tracking: "Where Did You Disappear?" , 2008, MELT '08.

[6]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[7]  Marcos R. Vieira,et al.  Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics , 2010, 2010 IEEE Second International Conference on Social Computing.

[8]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[9]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[10]  D. Lazer,et al.  Inferring Social Network Structure using Mobile Phone Data , 2006 .

[11]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Hui Zang,et al.  Bayesian Inference for Localization in Cellular Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[13]  Andrea Vitaletti,et al.  Cell-ID location technique, limits and benefits: an experimental study , 2004, Sixth IEEE Workshop on Mobile Computing Systems and Applications.

[14]  Eduard Heindl,et al.  Understanding the spreading patterns of mobile phone viruses , 2012 .

[15]  Carey L. Williamson,et al.  Characterizing and modeling user mobility in a cellular data network , 2005, PE-WASUN '05.

[16]  S. Chong,et al.  SLAW : A Mobility Model for Human Walks , 2009 .

[17]  Max Mühlhäuser,et al.  Opportunistic Data Dissemination Using Real-World User Mobility Traces , 2008, 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008).

[18]  Dieter Pfoser,et al.  Capturing the Uncertainty of Moving-Object Representations , 1999, SSD.

[19]  Enrique Frías-Martínez,et al.  User Modeling for Telecommunication Applications: Experiences and Practical Implications , 2010, UMAP.

[20]  Injong Rhee,et al.  SLAW: A New Mobility Model for Human Walks , 2009, IEEE INFOCOM 2009.

[21]  Lukas Kencl,et al.  Spatial extension of the Reality Mining Dataset , 2010, The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010).

[22]  Geoffrey M. Voelker,et al.  Access and mobility of wireless PDA users , 2003, MOCO.

[23]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[24]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[25]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[26]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[27]  Stephan Winter,et al.  Directed movements in probabilistic time geography , 2010, Int. J. Geogr. Inf. Sci..

[28]  Mike Williams.  How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics , 2010, 1006.3019.

[29]  Lukas Kencl,et al.  Performance study of active tracking in a cellular network using a modular signaling platform , 2010, MobiSys '10.

[30]  Rein Ahas,et al.  Mobile Positioning in Space–Time Behaviour Studies: Social Positioning Method Experiments in Estonia , 2007 .

[31]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[32]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[33]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.